Using machine learning and natural language processing to predict the stock market prices
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Updated
Mar 20, 2024 - Python
Using machine learning and natural language processing to predict the stock market prices
a project for learning more about the zenml stack
This project demonstrates how to build a machine learning pipeline to predict customer satisfaction. It utilizes ZenML for pipeline development and MLflow for deployment. The pipeline processes data, trains a model, and deploys it as a service. A Streamlit app provides a user interface for making predictions.
Training a neural network on the MNIST dataset using ZenML and PyTorch ignite
Charmed operator for ZenML Server
A Terraform module for provisioning and registering a cloud ZenML stack in AWS.
A Terraform module for provisioning and registering a cloud ZenML stack in GCP.
MLOps use case based on the Titanic Kaggle competition
Predicting how a customer will feel about a product before they even ordered it.
End-to-end machine learning project. Learn how to use ZenMl to build and deploy ML pipelines.
Stocks Forecasting for MAANG | MLOps using ZenML | Streamlit UI
A Terraform module for provisioning and registering a cloud ZenML stack in Azure.
Customer satisfaction prediction using Machine Learning algorithms and deploying and implementing MLOps with ZenML and MLflow
This project is designed to provide an end-to-end solution for forecasting how customers will feel about a product before they place an order
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